Evidence map›Paper›PMID 39219247›Full record

ArticleESC heart failure2024

Establishing a novel model to assess exercise capacity in chronic heart failure based on stress echocardiography.

Huan Cen, Sinan Chen, Shen Feng, Xiankun Chen, Huiying Zhu, Wei Jiang, Han Zhang, Hongmei Liu, Bo Liu, Weihui Lu and 1 more

Abstract read
In one paragraph

Article in ESC heart failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Huan CenDepartment of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Sinan ChenDepartment of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Shen FengSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, China.
Xiankun ChenState Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Huiying ZhuDepartment of Cardiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Wei JiangKey Unit of Methodology in Clinical Research, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
Han ZhangSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, China.
Hongmei LiuDepartment of Ultrasonography, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou, China.
Bo LiuDepartment of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Weihui LuDepartment of Cardiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Pengtao SunDepartment of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.ORCID 0000-0002-0848-3487

Funding

Guangdong Provincial Hospital of Chinese Medicine (GPHCM) YN10101910the Overseas High-profiled Experts Subsidy Project of Science and Technology Department of Guangdong ProvinceZhaoyang Talent Research Project of GPHCM ZY2022YL02
6 · The paper itself

Abstract

aimsThe aim of this study was to develop a simple, fast and efficient clinical diagnostic model, composed of exercise stress echocardiography (ESE) indicators, of the exercise capacity of patients with chronic heart failure (CHF) by comparing the effectiveness of different classifiers. METHODS AND

resultsEighty patients with CHF (aged 60 ± 11 years; 78% male) were prospectively enrolled in this study. All patients underwent both cardiopulmonary exercise test (CPET) and ESE and were divided into two groups according to the VE/VCO

conclusionsCompared with the LR, XGBT, CART and RF models, the LR model performed best at predicting the VE/VCO

Indexed as

Echocardiography, StressExercise ToleranceHeart FailureAgedChronic DiseaseExercise TestFemaleFollow-Up StudiesHumansMaleMiddle AgedProspective StudiesROC CurveStroke VolumeVentricular Function, LeftCardiopulmonary exercise testChronic heart failureExercise capacityExercise stress echocardiographyMachine learningVE/VCO2 slope

Identifiers

PMID39219247
PMCPMC11631253

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.